The microbiome of the buffalo digestive tract

Tong-qing, Feng; Wang, Teng; Gao, Na; Liu, Ziying; Cui, Kuiqing; Duan, Yiqian; Wu, Sicheng; Luo, Yuhong; Li, Zhipeng; Yang, Chengjian; Xu, Yixue; Lin, Bei; Yang, Liguo; Pauciullo, Alfredo; Shi, Deshun; Hua, Guohua; Chen, Wei‐Hua; Liu, Qingyou

doi:10.1038/s41467-022-28402-9

Cited by 56 publications

(56 citation statements)

References 108 publications

(135 reference statements)

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“…In addition, Malakar et al [ 20 ] compared fiber degradation in vitro by inoculum with cow’s and buffalo’s ruminal bacteria and fungi and reported that fiber degradation in buffalo was significantly higher than in dairy cows. A recent metagenomic study also reported that buffalo rumen microbiota has stronger fiber degradation and less methane production potential than dairy cow rumen microbiota [ 21 ]. Therefore, it is suggested that the dairy buffaloes may have different compositions and functions in rumen microbiota.…”

Section: Introductionmentioning

confidence: 99%

“…However, to the best of our knowledge, limited study has focused on identifying and understanding the establishment pattern and function of ruminal microbes in dairy buffaloes using high-throughput sequencing technology. Only a few studies have reported that analyzed the rumen microbiome of buffaloes using metagenomic approaches or 16S rRNA gene amplicon sequencing [ 21 , 22 , 23 , 24 , 25 , 26 , 27 ]. However, age-related rumen microbial functions have been rarely studied in buffaloes using a metagenomic approach.…”

Section: Introductionmentioning

confidence: 99%

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An Age Effect of Rumen Microbiome in Dairy Buffaloes Revealed by Metagenomics

Peng

Xue

et al. 2022

Microorganisms

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Age is an important factor in shaping the gut microbiome. However, the age effect on the rumen microbial community for dairy buffaloes remains less explored. Using metagenomics, we examined the microbial composition and functions of rumen microbiota in dairy Murrah buffaloes of different ages: Y (1 year old), M (3–5 years old), E (6–8 years old), and O (>9 years old). We found that Bacteroidetes and Firmicutes were the predominant phyla, with Prevotella accounting for the highest abundance at the genus level. The proportion of Bacteroides and Methanobrevibacter significantly increased with age, while the abundance of genus Lactobacillus significantly decreased with age (LDA > 3, p < 0.05). Most differed COG and KEGG pathways were enriched in Y with carbohydrate metabolism, while older buffaloes enriched more functions of protein metabolism and the processing of replication and repair (LDA > 2, p < 0.05). Additionally, the functional contribution analysis revealed that the genera Prevotella and Lactobacillus of Y with more functions of CAZymes encoded genes of glycoside hydrolases and carbohydrate esterases for their roles of capable of metabolizing starch and sucrose-associated oligosaccharide enzyme, hemicellulase, and cellulase activities than the other three groups (LDA > 2, p < 0.05), thus affecting the 1-year-old dairy buffalo rumen carbohydrate metabolism. This study provides comprehensive dairy buffalo rumen metagenome data and assists in manipulating the rumen microbiome for improved dairy buffalo production.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Age Effect of Rumen Microbiome in Dairy Buffaloes Revealed by Metagenomics

Peng

Xue

et al. 2022

Microorganisms

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“…And MetaSNV (v 1.0.3) 23 was used to calculate the average coverage for each MAGs in BAM files. As the cut-off of MAG’s presence/absence, average coverage was adjusted using data size of each sample, and copresent MAGs were defined as the MAGs presented (average coverage > 1) 20 in every sample of cow, goat, co-inoculum group, and initial inocula. The rest of MAGs couldn’t meet the threshold in all samples were defined as non-copresent MAGs.…”

Section: Methodsmentioning

confidence: 99%

“…Estimated genome size of MAG was corrected as described in previous study using completeness and contamination 16 . In addition, the obtained MAGs were compared to the rumen MAGs set collected from previous studies (n = 22506) 9,[17][18][19][20] and the GTDB representative genomes (n = 31910) using fastANI (v 1.1) 21 .…”

Section: De Novo Metagenome Assembly Binning and Genome Annotationmentioning

confidence: 99%

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Copresent microbiome and short-chain fatty acids profiles of plant biomass utilization in rumen simulation technique system

Shi

Zhang

Wang

et al. 2022

Preprint

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Rumen contents have considerable utility in converting plant biomass to short-chain fatty acids (SCFAs) when used as inoculum for in vitro fermentation. To better understand the microbial communities and their functions when in vitro ruminal fermentation, the microbiome and SCFAs production were investigated using rumen simulation technique (RUSITEC) system which was inoculated/co-inoculated with rumen contents from goat and cow. This study reconstructed 1677 microbial metagenome-assembled genomes (MAGs) from metagenomic sequencing. The copresent microbiome containing 298 MAGs were found in metagenomic data of these contents and previous ruminal representative samples. These copresent MAGs were overrepresented in decomposing various substrates, especially pectin and xylan. Additionally, the SCFAs productions in RUSITEC were linked with copresent MAGs. Copresent MAGs obtained from this study shows promise to point out the direction for further research on in vitro ruminal fermentation, and enables a better understanding of rumen microbiotal structures and functions under in vitro condition.

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Recovering High‐Quality Host Genomes from Gut Metagenomic Data through Genotype Imputation

2022

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Metagenomic datasets of host‐associated microbial communities often contain host DNA that is usually discarded because the amount of data is too low for accurate host genetic analyses. However, genotype imputation can be employed to reconstruct host genotypes if a reference panel is available. Here, the performance of a two‐step strategy is tested to impute genotypes from four types of reference panels built using different strategies to low‐depth host genome data (≈2× coverage) recovered from intestinal samples of two chicken genetic lines. First, imputation accuracy is evaluated in 12 samples for which both low‐ and high‐depth sequencing data are available, obtaining high imputation accuracies for all tested panels (>0.90). Second, the impact of reference panel choice in population genetics statistics on 100 chickens is assessed, all four panels yielding comparable results. In light of the observations, the feasibility and application of the applied imputation strategy are discussed for different species with regard to the host DNA proportion, genomic diversity, and availability of a reference panel. This method enables leveraging insofar discarded host DNA to get insights into the genetic structure of host populations, and in doing so, facilitates the implementation of hologenomic approaches that jointly analyze host and microbial genomic data.

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The microbiome of the buffalo digestive tract

Cited by 56 publications

References 108 publications

An Age Effect of Rumen Microbiome in Dairy Buffaloes Revealed by Metagenomics

An Age Effect of Rumen Microbiome in Dairy Buffaloes Revealed by Metagenomics

Copresent microbiome and short-chain fatty acids profiles of plant biomass utilization in rumen simulation technique system

Recovering High‐Quality Host Genomes from Gut Metagenomic Data through Genotype Imputation

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